docstr: Generate embeddings for a batch of utterances
  x: BxTxD
docstr_tok: ["Generate","embeddings","for","a","batch","of","utterances","x",":","BxTxD"]
code: def batch_compute_embedding(
      self, x, seq_lens, num_frames=160, overlap=0.5
  ):
      """
      Generate embeddings for a batch of utterances
      x: BxTxD
      """
      num_overlap = num_frames * overlap
      max_len = x.shape[1]
      embed = None
      num_iters = seq_lens / (num_frames - num_overlap)
      cur_iter = 0
      for offset in range(0, max_len, num_frames - num_overlap):
          cur_iter += 1
          end_offset = min(x.shape[1], offset + num_frames)
          frames = x[:, offset:end_offset]
          if embed is None:
              embed = self.inference(frames)
          else:
              embed[cur_iter <= num_iters, :] += self.inference(
                  frames[cur_iter <= num_iters, :, :]
              )
      return embed / num_iters
  
file_path: TTS/TTS/speaker_encoder/model.py
imports: import torch
  from torch import nn
label_desc: Use image node_id 6 for calling the SpeakerEncoder obj's underlying member method code with example usage: obj.batch_compute_embedding(x, seq_lens, num_frames, overlap) and returns: unknown